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Lehrstuhl für Mathematik, insbesondere

Numerische und Angewandte Mathematik

Forschungsschwerpunkt

Genetische Algorithmen

When Holland (1975) proposed genetic algorithms, he envisioned them as methods that were going to be efficient, easy to use, and applicable to a wide range of problems. But one thing that stands out from the current literature, is that genetic algorithms seem to require quite a bit of expertise in order to make them work well for a particular application. The expertise is needed because users are generally clueless on how to decide among the various codings and operators, as well as on deciding on a good set of parameter values for the GA. In the end, instead of being a robust and an easy-to-use method, the genetic algorithm turns out to be a method that needs a lot of tuning and parameter fiddling. This state of affairs is a kind of a paradox, and contradicts Holland's original goals.

The decisions that a user must make before applying a GA can be grouped in two categories. The first, is the choice of an appropriate coding and operators. The second, is the choice of appropriate parameter settings.

From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press a start button. We investigate the development of a GA that fulfils this requirement. It has no parameters whatsoever. The development of the algorithm takes into account several aspects of the theory of GAs, including previous research works on population sizing, the schema theorem, building block mixing, and genetic drift.

If Genetic Algorithms should be made accessible for a broader public, further convergence criteria and proofs must be developed - in particular in order to place the selection of numerous parameters on a solid base.
 

Related Literature (Some of the publications which I have read.)

  1. Antonisse, J. (1989). A new interpretation of schema notation that overturns the binary encoding constraint. In Schaffer, J. D., editor, Proceedings of the Third International Conference on Genetic Algorithms (ICGA ’89), pages 86–91. Morgan Kaufmann Publishers.
  2. Bäck, T. (1991). Optimization by means of genetic algorithms. In Köhler, E., editor, 36. Internationales wissenschaftliches Kolloquium, pages 163–169. Technische Universität Ilmenau.
  3. Bäck, T. (1992a). The interaction of mutation rate, selection, and self-adaption within a genetic algorithm. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature 2, pages 85–94. Elsevier Science Publishers.
  4. Bäck, T. (1992b). Self-adaptation in genetic algorithms. In Proceedings of the 1st European Conference on Artificial Life (1991), pages 263–271. MIT Press.
  5. Bäck, T. (1993). Optimal mutation rates in genetic search. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 2–9, San Mateo, CA. Morgan Kaufmann Publishers.
  6. Bäck, T., Hammel, U., and Schwefel, H.-P. (1997). Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1):3–17.
  7. Bäck, T. and Hoffmeister, F. (1991). Extended selection mechanisms in genetic algorithms. In Belew, R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA ’91), pages 92–99. Morgan Kaufmann Publishers.
  8. Bäck, T., Hoffmeister, F., and Schwefel, H.-P. (1991). A survey of evolution strategies. In Belew, R. K. and Booker, L. B., editors, Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA ’91), pages 2–9. Morgan Kaufmann Publishers.
  9. Bäck, T. and Schwefel, H.-P. (1995a). Evolution Strategies I: Variants and their computational implementation. In Winter, G., Périeaux, J., Galá, M., and Cuesta, P., editors, Genetic Algorithms in Engineering and Computer Science, pages 111–126, Chicester. Wiley.
  10. Bäck, T. and Schwefel, H.-P. (1995b). Evolution Strategies II: Theoretical aspects implementation. In Winter, G., Périeaux, J., Galá, M., and Cuesta, P., editors, Genetic Algorithms in Engineering and Computer Science, pages 127–140, Chicester. Wiley. 
  11. Beasley, D., Bull, D. R., and Martin, R. R. (1993a). An overview of genetic algorithms: Part 1, fundamentals. University Computing, 15(2):58–69.
  12. Beasley, D., Bull, D. R., and Martin, R. R. (1993b). An overview of genetic algorithms: Part 2, research topics. University Computing, 15(4):170–181. 
  13. Beasley, D., Bull, D. R., and Martin, R. R. (1993c). A sequential niche technique for multimodal function optimization. Evolutionary Computation, 1(2):101–125.
  14. Belding, T. C. (1995). The distributed genetic algorithm revisited. In Eshelman, D., editor, Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA ’95), San Francisco. Morgan Kaufmann Publishers. 
  15. Benyahia, I. and Potvin, J.-Y. (1995). Generalization and refinement of route construction heuristics using genetic algorithms. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 39–43. 
  16. Bertoni, A. and Dorigo, M. (1993). Implicit parallelism in genetic algorithms. Artificial Intelligence, 2(61):307–314.
  17. Brittain, D., Sims Williams, J., and McMahon, C. A. (1997). A genetic algorithm approach to planning the telecommunications access. In Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA ’97). Morgan Kaufmann Publishers.
  18. Bruns, R. (1995). Integration of constraint solving techniques in genetic algorithms. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 33–38.
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  21. Chang, C. S., Wang, W., Liew, A. C., Wen, F. S., and Srinivasan, D. (1995). Genetic algorithm based bicriterion optimisation for traction substations in dc railway system. In Proceedings of the IEEE International conference on Evolutionary Computation (ICEC 95), pages 11–16.
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  31. Eddelbüttel, D. (1992). A genetic algorithm for passive management. Master’s thesis, GREQE-EHESS, Centre de la Vieille Charit’e, Marseille. 
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  33. Fang, H.-L., Ross, P., and Corne, D. (1993). A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA ’93), pages 375–382, San Mateo. Morgan Kaufmann Publishers.
  34. Field, P. (1994). Walsh and partition functions made easy. In missing. Presented as a poster at the 1994 AISB Workshop on Evolutionary Computing.
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  45. Goldberg, D. E. (1994b). The existential pleasures of genetic algorithms. Technical Report IlliGAL Report No. 94010, Illinois Genetic Algorithms Laboratory (IlliGAL).
  46. Goldberg, D. E. (1994c). First flights at genetic-algorithm kitty hawk. Technical Report IlliGAL Report No. 94008, Illinois Genetic Algorithms Laboratory (IlliGAL).
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